Using Hierarchical Clustering to organize and explore modern and ancient DNA data using G25 coordinates

require(rio)
require(tidyverse)
require(ggthemes)
require(pvclust)
require(ggdendro)
require(geomtextpath)

Loading required package: ggthemes

Loading required package: pvclust

Loading required package: ggdendro

Loading required package: geomtextpath

Load the data

myd = import("~/Documents/Ancestry/Genetics/G25/Data/TXT/By Age/Ancient_sampled.txt",fill = TRUE)
myd = myd %>% bind_rows(import("~/Documents/Ancestry/Genetics/G25/Data/TXT/By Age/Modern_Eurasia_sampled.txt",fill = TRUE))

myd %>% sample_n(15)

A data.frame: 15 × 26

V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	⋯	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26
<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
Greek_Crete:B_Crete-1	0.112685	0.138112	-0.008674	-0.040698	0.004616	-0.011992	-0.000705	-0.005307	-0.000614	⋯	0.005569	0.016168	0.006461	0.006913	-0.004627	0.002121	0.001484	-0.008751	-0.004217	-0.000359
Greek_Argolis:ARGO-19	0.121791	0.133034	0.004148	-0.023902	0.016926	-0.001394	0.002115	-0.001846	-0.006545	⋯	0.012198	0.017863	0.005321	0.008422	-0.007128	-0.007986	0.000989	-0.003451	0.007350	-0.008382
Spain_SE_Iberia_BA_Valencian:LHO003	0.129758	0.154360	0.059208	0.011628	0.063704	-0.011435	-0.002820	0.003461	0.044586	⋯	0.012066	0.018384	-0.002914	-0.001383	0.005503	0.005865	0.005441	-0.010106	-0.026751	0.007784
Greek_West_Taygetos:MMA-26	0.117238	0.139128	0.009428	-0.020995	0.023081	-0.005578	0.008695	-0.002077	-0.006340	⋯	0.003845	0.010170	-0.000887	0.006662	-0.003877	-0.004367	0.001607	0.005300	0.004097	0.000838
Azerbaijani_Dagestan:AZR-0865	0.096750	0.097491	-0.052043	-0.029393	-0.041546	0.005578	0.010105	-0.009230	-0.023929	⋯	-0.004375	-0.011083	0.003927	0.000754	-0.006753	-0.003119	0.005317	-0.000493	-0.007109	-0.003233
Albanian:ALB220	0.109270	0.147252	0.013953	-0.021641	0.023697	-0.009761	0.006345	0.005769	-0.005522	⋯	0.003447	0.013299	-0.004054	0.014330	-0.001251	-0.008235	0.002349	0.003328	0.006145	0.001078
German_Erlangen:GSM1658634	0.130897	0.134050	0.057699	0.042959	0.033545	0.021753	-0.004465	0.002538	0.001841	⋯	0.001856	0.008345	-0.003801	0.003771	0.003752	0.002121	-0.005317	-0.006162	-0.006868	-0.000479
Serbian:726	0.126344	0.136081	0.038843	0.010982	0.035083	-0.000558	0.000705	0.008538	-0.005931	⋯	0.006364	0.005998	0.009755	0.005405	0.002626	-0.005865	-0.002968	0.004314	-0.000723	-0.001796
Cossack_Ukrainian:GS000035238	0.135449	0.116786	0.071653	0.060078	0.033852	0.023148	0.011281	0.010615	0.003068	⋯	0.001989	0.000391	0.005194	0.003520	0.003377	-0.009608	-0.002349	0.004067	-0.006868	0.000599
Basque_Navarre_North:5991	0.136588	0.149283	0.064111	0.008721	0.057241	0.000558	-0.007990	-0.002077	0.034155	⋯	0.005569	-0.002347	0.001140	-0.003017	-0.002751	0.009733	0.012736	-0.012202	-0.002410	0.005389
Lithuanian_RA:LTG-325	0.135449	0.125926	0.088246	0.076228	0.037853	0.034025	0.010340	0.020076	0.001023	⋯	-0.000530	0.009648	-0.004434	0.000880	0.005002	-0.003868	-0.007172	0.007888	-0.009399	0.001197
French_Auvergne:C_16	0.129758	0.147252	0.050157	0.015827	0.043393	0.003626	-0.007990	0.003923	0.011453	⋯	-0.007425	-0.001695	-0.002787	0.004399	0.004502	-0.001123	-0.002226	-0.003081	0.001807	0.001317
Georgian_Meskheti:MSKH2	0.108132	0.138112	-0.065619	-0.068476	-0.029236	-0.020917	0.003760	-0.006461	-0.032315	⋯	0.009944	0.017471	-0.001900	-0.002137	0.001626	0.001872	0.000371	0.002342	-0.000241	-0.004910
Italy_Medieval_EarlyModern.SG:R1285.SG	0.114961	0.150298	0.010182	-0.030362	0.028313	-0.010598	-0.003995	-0.003692	0.018203	⋯	-0.012066	-0.007432	0.003927	0.007793	-0.008754	0.002246	0.014220	-0.004190	-0.001084	0.000359
Greek_Crete:805	0.114961	0.144205	-0.013199	-0.041344	0.009232	-0.011435	0.006580	0.001154	-0.014112	⋯	0.009679	0.021383	-0.003294	0.008547	-0.008629	-0.002870	0.003462	0.005423	0.004217	0.002275

Denormalize the sample identifiers

myd = myd %>% mutate(loc = str_extract(V1,"^\\w+?(?=_)"),
                     era = str_extract(V1,"(?<=_)\\w[^_]+?(?=[:.])"),
                     sample = str_extract(V1,"(?<=:)\\w+?(?=(_|\\.|$))"),
                     desc = paste(loc,era,sep = '_'),.before = V1)

myd %>% select(loc,era,sample,desc,V1) %>% sample_n(15)

A data.frame: 15 × 5

loc	era	sample	desc	V1
<chr>	<chr>	<chr>	<chr>	<chr>
England	Saxon	I20646	England_Saxon	England_EarlyMedieval_Saxon:I20646
Sweden	Viking	VK355	Sweden_Viking	Sweden_Viking.SG:VK355_noUDG.SG
French	Occitanie	T	French_Occitanie	French_Occitanie:T_11
NA	NA	NA	NA_NA	Vepsian:KAR-190
Russia	Catacomb	RK4002	Russia_Catacomb	Russia_Steppe_Catacomb:RK4002
Russian	Kursk	RussianKursk18	Russian_Kursk	Russian_Kursk:RussianKursk18
Germany	EarlyMedieval	NW255a	Germany_EarlyMedieval	Germany_EarlyMedieval.SG:NW255a_noUDG.SG
NA	NA	Polish18	NA_NA	Polish:Polish18
Italy	EarlyModern	R58	Italy_EarlyModern	Italy_Medieval_EarlyModern.SG:R58.SG
Georgian	Jew	GeorgianJew1607	Georgian_Jew	Georgian_Jew:GeorgianJew1607
Italian	Veneto	KF1803151	Italian_Veneto	Italian_Veneto:KF1803151
Georgian	Imer	GEO73	Georgian_Imer	Georgian_Imer:GEO73_georgian_imer
Italian	Calabria	BEL57	Italian_Calabria	Italian_Calabria:BEL57
NA	NA	NA	NA_NA	Lak:DAG-388
Albania	Modern	I15706	Albania_Modern	Albania_Modern:I15706

Summarize and transpose

mydd = t(myd %>% select(-loc,-era,-sample,-V1) %>% group_by(desc) %>% summarize_all(median))

mydd = as_tibble(mydd)
colnames(mydd) = mydd[1,]
mydd %>% sample_n(10)

Warning message:
“The `x` argument of `as_tibble.matrix()` must have unique column names if
`.name_repair` is omitted as of tibble 2.0.0.
ℹ Using compatibility `.name_repair`.”

A tibble: 10 × 562

ALB_PostMA	ARM_Beniamin	ARM_EIA	ARM_LBA	ARM_LBA-EIA	ARM_MBA	AUS_112BP	AUT_Wels	Abkhasian_Gudauta	Albania_Modern	⋯	Ukrainian_Chernihiv	Ukrainian_Dnipro	Ukrainian_Lviv	Ukrainian_NA	Ukrainian_Rivne	Ukrainian_Sumy	Ukrainian_Zakarpattia	Ukrainian_Zhytomyr	Uppland_VA	Uppsala_VA
<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	⋯	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
0.0085065	-0.0156175	0.0075300	0.0000000	-0.0009765	-0.0064140	-0.3360630	-0.0019520	-0.0058565	-0.0039040	⋯	0.0251000	0.0209170	0.0191040	-0.0161760	0.0209170	0.0242635	0.0170120	0.0211960	0.0220320	0.0298410
0.0044370	0.0060395	0.0035740	-0.0052380	0.0009860	0.0097370	0.0044370	-0.0020950	0.0037590	0.0034510	⋯	0.0087510	0.0041900	0.0032045	0.0085040	0.0053000	0.0050530	0.0036970	0.0044370	-0.0002460	0.0019720
0.0025140	-0.0028285	0.0033310	-0.0021995	-0.0022000	0.0038970	0.0035200	-0.0061590	-0.0103075	0.0008800	⋯	0.0026400	0.0032680	0.0039595	0.0035200	0.0040220	0.0040850	0.0017600	0.0040220	0.0070390	0.0084220
0.0196815	-0.0128475	-0.0236905	-0.0193170	-0.0203195	-0.0169480	-0.0061960	0.0156720	-0.0232350	0.0058320	⋯	-0.0244200	-0.0280640	-0.0142145	0.0116630	-0.0224150	-0.0224150	-0.0112990	-0.0264240	-0.0107520	-0.0030980
-0.0013490	0.0044960	0.0055450	0.0017985	0.0020235	-0.0040460	-0.0019480	0.0049460	0.0133380	0.0046460	⋯	-0.0074930	-0.0092920	-0.0060695	0.0010490	-0.0074930	-0.0097415	-0.0076430	-0.0110900	-0.0089920	-0.0059950
-0.0152685	-0.0046820	0.0057005	0.0070575	0.0057005	0.0122150	-0.0038000	0.0054290	0.0082110	-0.0158790	⋯	-0.0128930	-0.0093650	-0.0111290	-0.0020360	-0.0135720	-0.0112650	-0.0114010	-0.0093650	0.0152010	0.0080070
0.0051150	0.0002435	0.0057645	0.0029230	0.0042220	0.0077950	-0.0100680	-0.0034100	0.0030850	0.0035730	⋯	-0.0035730	-0.0021110	-0.0060085	-0.0008120	-0.0030850	-0.0030045	-0.0019490	-0.0022730	-0.0074700	-0.0061710
0.0053020	-0.0020485	-0.0046990	0.0019885	0.0056635	0.0056630	-0.0031330	0.0087960	-0.0051210	-0.0027710	⋯	-0.0057840	-0.0048200	-0.0036750	-0.0007230	-0.0051810	-0.0095190	-0.0008430	-0.0036150	0.0034940	0.0120500
0.0028245	-0.0022300	-0.0032705	-0.0047575	-0.0052775	-0.0004460	0.0081760	-0.0123390	-0.0266105	0.0041620	⋯	0.0162040	0.0185830	0.0111500	0.0007430	0.0126360	0.0147175	0.0086220	0.0136770	0.0011890	0.0013380
-0.0082540	0.0003125	-0.0006880	0.0042520	-0.0017510	-0.0041270	0.0023760	-0.0082540	0.0269505	-0.0005000	⋯	0.0020010	-0.0015010	0.0004375	0.0003750	-0.0007500	-0.0014385	0.0002500	0.0012510	0.0032520	0.0122560

Run clustering

myfit = pvclust(mydd[2:nrow(mydd),],method.hclust = "complete",method.dist = "euclidean",nboot = 1)

Bootstrap (r = 0.48)... Done.
Bootstrap (r = 0.6)... Done.
Bootstrap (r = 0.68)... Done.
Bootstrap (r = 0.8)... Done.
Bootstrap (r = 0.88)... Done.
Bootstrap (r = 1.0)... Done.
Bootstrap (r = 1.08)... Done.
Bootstrap (r = 1.2)... Done.
Bootstrap (r = 1.28)... Done.
Bootstrap (r = 1.4)... Done.

Pull out the dendrogram data

ddata = dendro_data(myfit$hclust,type = "rectangle",uniform = TRUE,compress = TRUE)

options(repr.plot.width = 12, repr.plot.height = 12, repr.plot.res = 400)

sdata = segment(ddata)
ldata = label(ddata)
p = ggplot(sdata) + 
    geom_segment(aes(x = x,y = y,xend = xend,yend = yend),color = "wheat") + 
    geom_textvline(data = ldata,aes(xintercept = x,label = label,color = as.factor(str_extract(label,'^\\w+?(?=_)'))),size = 1.5,linetype = 0,hjust = 1.1) + 
    scale_y_reverse(expand = c(0.2, 0)) + 
    coord_curvedpolar() + 
    theme_void()
p + guides(color = "none") + scale_color_viridis_d(option = "D") + theme(plot.background = element_rect(fill = "gray10",color = "gray10"),plot.margin = margin(0,0,0,0,"cm"))

Adding Y and mtDNA etc

ydata = as_tibble(import("~/Documents/Ancestry/Raw Data/Archeological Samples/aDNA-Y-mt/all-ancient-dna.xlsx"))

Warning message:
“Expecting numeric in AA1070 / R1070C27: got '...'”
Warning message:
“Expecting numeric in AA1476 / R1476C27: got '...'”
Warning message:
“Expecting numeric in AA1477 / R1477C27: got '...'”
Warning message:
“Expecting numeric in AO2887 / R2887C41: got '..'”
Warning message:
“Expecting numeric in AA4167 / R4167C27: got '...'”
Warning message:
“Expecting numeric in AO5085 / R5085C41: got '..'”
Warning message:
“Coercing text to numeric in AO7537 / R7537C41: '0.088'”
Warning message:
“Expecting numeric in AO8135 / R8135C41: got '..'”
Warning message:
“Expecting numeric in AO8222 / R8222C41: got '..'”
Warning message:
“Expecting numeric in AO8246 / R8246C41: got '..'”
Warning message:
“Expecting numeric in AO11704 / R11704C41: got '..'”
Warning message:
“Expecting numeric in AO11886 / R11886C41: got '..'”
Warning message:
“Coercing text to numeric in AO14505 / R14505C41: '0.778'”

ydata %>% sample_n(10)

A tibble: 10 × 64

Object-ID	Colloquial-Skeletal	Latitude	Longitude	Sex	mtDNA-coverage	mtFAR	mtDNA-haplogroup	mtDNA-Haplotree	mt-FTDNA	⋯	Location	SiteID	Country	LP	Skin	Hair	Eye	P1104A	Other	Index
<chr>	<chr>	<dbl>	<dbl>	<chr>	<chr>	<dbl>	<chr>	<chr>	<chr>	⋯	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<dbl>	<chr>	<dbl>
Ramesses_III	Ramesses III	25.73989	32.607778	M	NA	NA	NA	NA	NA	⋯	KV11, Valley of the Kings	NA	Egypt	NA	NA	NA	NA	NA	NA	1521
I3342	R08	30.26210	79.731719	M	767	0.0356471	M3a2	M3a2	https://www.familytreedna.com/public/mt-dna-haplotree/M;name=M3a2	⋯	Roopkund Lake	NA	India	NA	NA	NA	NA	NA	NA	8000
JAG001	AT-878	47.81308	108.531944	M	NA	0.7116232	F1b1f	F1b1f	https://www.familytreedna.com/public/mt-dna-haplotree/F;name=F1b1f	⋯	Jargalantyn Am (Jargalantyn Khondii), Khentii	Circular grave; Grave 151	Mongolia	NA	NA	NA	NA	NA	NA	11096
Uyelgi8	ULG16	55.84855	61.552110	F	NA	0.2602740	H40b	H40b	https://www.familytreedna.com/public/mt-dna-haplotree/H;name=H40b	⋯	Uyelgi, Chelyabinsk region	Kurgan 29; Grave 1	Russia	NA	NA	NA	NA	NA	NA	10463
CSP005	Caspan 6 mound 1, Skull #2	44.49432	78.266442	F	59.487499999999997	0.9771836	T2b4e	T2b4e	https://www.familytreedna.com/public/mt-dna-haplotree/T;name=T2b4e	⋯	Caspan 6 mound 1, Skull #2	NA	Kazakhstan	NA	NA	NA	NA	NA	NA	14111
NOR3-6	NOR3-6	48.44526	7.671180	F	27	0.3926496	H7d	H7d	https://www.familytreedna.com/public/mt-dna-haplotree/H;name=H7d	⋯	Nordhouse	Nordhouse	France	NA	NA	NA	NA	NA	NA	7663
MJ-19	46	48.80342	30.509367	F	..	0.4851641	H1n6	H1n6	https://www.familytreedna.com/public/mt-dna-haplotree/H;name=H1n6	⋯	Legedzine	Grave 20	Ukraine	NA	NA	NA	NA	NA	NA	8086
AC22	NA	47.06120	19.275400	NA	NA	0.4791743	M7c1b2b	M7c1b2b	https://www.familytreedna.com/public/mt-dna-haplotree/M;name=M7c1b2b	⋯	Kunpeszér	NA	Hungary	NA	NA	NA	NA	NA	NA	10407
I1674	SG21	34.50000	47.960000	M	334	1.0000000	I1c	I1c	https://www.familytreedna.com/public/mt-dna-haplotree/I;name=I1c	⋯	Seh Gabi	NA	Iran	NA	Intermediate	Brown/Dark Brown	Brown	NA	NA	3962
MX182	126744	47.26207	7.659835	NA	..	0.6141207	K1a	K1a	https://www.familytreedna.com/public/mt-dna-haplotree/K;name=K1a	⋯	Oberbipp Horgen (CH)	NA	Switzerland	NA	NA	NA	NA	NA	NA	10836

colnames(ydata)

1. 'Object-ID'
2. 'Colloquial-Skeletal'
3. 'Latitude'
4. 'Longitude'
5. 'Sex'
6. 'mtDNA-coverage'
7. 'mtFAR'
8. 'mtDNA-haplogroup'
9. 'mtDNA-Haplotree'
10. 'mt-FTDNA'
11. 'mtree'
12. 'mt-YFull'
13. 'FTDNA-mt-Haplotree'
14. 'mt-Simple'
15. 'mt-Symbol'
16. 'HVS-I'
17. 'HVS-II'
18. 'HVS-NO'
19. 'mt-SNPs'
20. 'Responsible-mtDNA'
21. 'Y-DNA'
22. 'Y-New'
23. 'SNP-positive'
24. 'SNP-negative'
25. 'SNP-dubious'
26. 'NRY'
27. 'Y-FAR'
28. 'Y-Simple'
29. 'YTree'
30. 'Y-Haplotree-Variant'
31. 'Y-Haplotree-Public'
32. 'Y-FTDNA'
33. 'YFull'
34. 'Y-YFull'
35. 'ISOGG2019'
36. 'FTDNA-Y-Haplotree'
37. 'Y-Symbol'
38. 'Y-Symbol2'
39. 'Responsible-SNP'
40. 'SNPs'
41. 'Autosomal-Coverage'
42. 'Damage-Rate'
43. 'Assessment'
44. 'Kinship-Notes'
45. 'Source'
46. 'Method-Date'
47. 'Date'
48. 'Mean'
49. 'CalBC_top'
50. 'CalBC_bot'
51. 'Age'
52. 'Simplified_Culture'
53. 'Culture_Grouping'
54. 'Label'
55. 'Location'
56. 'SiteID'
57. 'Country'
58. 'LP'
59. 'Skin'
60. 'Hair'
61. 'Eye'
62. 'P1104A'
63. 'Other'
64. 'Index'

ydata %>% select(`Y-Simple`,`YTree`,`YFull`,`ISOGG2019`,`Y-Haplotree-Variant`,`Y-Haplotree-Public`,`Y-Symbol`,`Y-Symbol2`) %>% sample_n(10)

A tibble: 10 × 8

Y-Simple	YTree	YFull	ISOGG2019	Y-Haplotree-Variant	Y-Haplotree-Public	Y-Symbol	Y-Symbol2
<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
R1bL23	R1b-L23	R-L23	R1b1a1b1(xR1b1a1b1a1a1c1)	R-L23	R-L23	R1b	R1bL23
NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA
Q1bY4276	Q1b-Z35703*	Q-Y4303*	Q1b1a1a2a~..	Q-Z35703	Q-Z35703	Q	Q1b
NA	NA	NA	NA	NA	NA	NA	NA
R1bP312	R1b-P312	R-P312	R1b1a1b1a1a2	R-P312	R-P312	R1b	R1bP312
H2	H2	H-P96	H2	H-P96	H-P96	H	H
NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA

nrow(ydata)
ydata %>% select(`Object-ID`,`Date`,`Mean`,`Simplified_Culture`,`Label`,`Location`,`Country`) %>% sample_n(10)

15460

A tibble: 10 × 7

Object-ID	Date	Mean	Simplified_Culture	Label	Location	Country
<chr>	<chr>	<dbl>	<chr>	<chr>	<chr>	<chr>
G7	1000 AD	1000	Viking	NA	Galgedil	Denmark
I11249	3500-2700 BCE	-3100	Iberia_MLN	Spain_MLN	Jentillarri, Enirio-Aralar, Gipuzkoa, Basque Country	Spain
SzO/81	600–650/660	645	Avar	Avar_Early	Szegvár-Oromdűlő/81	Hungary
BENZ 18	3101-2919 calBC 1σ (4418 ± 65 BP)	-3010	CEU_MN	NA	Benzingerode I	Germany
LICOL93	570-630/640 AD	605	Germanic	Italy_North_EarlyMedieval_Langobards	Collegno	Italy
LJZ–M67–A	3100-1800 BP	-500	China_IA	NA	Jinan_Liujiazhuang site	China
Takasago14	3000-2500 BP	-750	Japan_Jomon	NA	Takasago	Japan
PB1794	3642-3377 calBCE (4764±54 BP, UBA-39197)	-3536	Ireland_MN	Ireland_MN.SG	Parknabinnia, Clare	Ireland
JK2974	889-803 cal BC	-846	Egypt_Pre_Ptolemaic	NA	Abusir el-Meleq	Egypt
Bla28	3196 ± 103 calBC	-3196	CEU_LN	Germany_LN	Blätterhöhle at Hagen, Sauerland region, Westphalia	Germany

ydata = ydata %>% select(`Object-ID`,`Mean`,`Simplified_Culture`,`Location`,`Country`,`Y-Symbol`,`YTree`)

myd = myd %>% inner_join(ydata,by = c("sample" = "Object-ID"))

Warning message in inner_join(., ydata, by = c(sample = "Object-ID")):
“Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 356 of `x` matches multiple rows in `y`.
ℹ Row 4282 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
  "many-to-many"` to silence this warning.”

colnames(myd)
myd %>% select(sample,desc,`Mean`,`Simplified_Culture`,`Location`,`Country`,`Y-Symbol`,`YTree`) %>% sample_n(10)

1. 'loc'
2. 'era'
3. 'sample'
4. 'desc'
5. 'V1'
6. 'V2'
7. 'V3'
8. 'V4'
9. 'V5'
10. 'V6'
11. 'V7'
12. 'V8'
13. 'V9'
14. 'V10'
15. 'V11'
16. 'V12'
17. 'V13'
18. 'V14'
19. 'V15'
20. 'V16'
21. 'V17'
22. 'V18'
23. 'V19'
24. 'V20'
25. 'V21'
26. 'V22'
27. 'V23'
28. 'V24'
29. 'V25'
30. 'V26'
31. 'Mean'
32. 'Simplified_Culture'
33. 'Location'
34. 'Country'
35. 'Y-Symbol'
36. 'YTree'

A data.frame: 10 × 8

sample	desc	Mean	Simplified_Culture	Location	Country	Y-Symbol	YTree
<chr>	<chr>	<dbl>	<chr>	<chr>	<chr>	<chr>	<chr>
RISE1166	Poland_GlobularAmphora	-2924	Globular_Amphora	Koszyce, site 3	Poland	NA	NA
I0984	Russia_Sintashta	-1920	Sintashta	Kamennyi Ambar 5 Cemetery	Russia	R1a	R1a-Z2124(xF1345;xS23592, etc.)
VK153	POL_VA	1000	Viking	Bodzia	Poland	R1a	R1a-M198
VK269	SWE_VA	950	Viking	Karda	Sweden	R1b	R1b-M269
R969	Italy_EarlyModern	1650	Italy_Renaissance	Tivoli Palazzo Cianti	Italy	J	J2a-Z28598*
ros005	Sweden_FBC	-3005	Nordic_MN	Rössberga	Sweden	I	I2-pre-S2742
R1286	Italy_oCentralEuropean	1430	Italy_MA	Cancelleria	Italy	I	I1-FT151924
I3600	Germany_BellBeaker	-2225	Bell_Beaker	Alburg-Lerchenhaid, Spedition Häring, Stkr. Straubing, Bavaria	Germany	NA	NA
I7282	Czech_BellBeaker	-2250	Bell_Beaker	Radovesice	Czech Republic	R1b	R1b-L2
I6349	Mongolia_NA	-831	Slab_Grave	Sükhbaatar aimag, Dariganga sum, Baaran-Khavtsal	Mongolia	Q	Q1a-(pre-?)M120(FT9308?)

myd = myd %>% mutate(yc = paste(Country,`Y-Symbol`,sep = "_"),y = ifelse(is.na(`Y-Symbol`),'??',`Y-Symbol`))

Automating the process

myfun = function(somedata,size = 3,polar = TRUE,modulo = FALSE) {
    mydd = as_tibble(somedata)
    print(dim(mydd))
    print(mydd %>% head)
    myfit = pvclust(mydd,method.hclust = "complete",method.dist = "euclidean",nboot = 1)
    ddata = dendro_data(myfit$hclust,type = "rectangle",uniform = TRUE,compress = TRUE)
    print(summary(ddata$segments))
    print(summary(ddata$labels))
    sdata = segment(ddata) 
    ldata = label(ddata)
    if (modulo) {ldata = ldata %>% dplyr::filter(x%%5 == sample(0:4,1))}
    p = ggplot(sdata) + 
        geom_segment(aes(x = x,y = y,xend = xend,yend = yend),color = "wheat") + 
        geom_textvline(data = ldata,aes(xintercept = x,label = label,color = label),size = size,linetype = 0,hjust = 1.1) + 
        scale_y_reverse(expand = c(0.2, 0)) + 
        scale_color_viridis_d(option = "D") + 
        guides(color = "none") +  
        theme_void() + 
        theme(panel.background = element_rect(fill = "gray10",color = "gray10")) + 
        theme(plot.background = element_rect(fill = "gray10",color = "gray10"))
    ifelse(polar,print(p + coord_curvedpolar()),print(p) + theme_dendro())
}

mydfun = function(somedata,variable) {
    myvars = c(variable,paste0("V",2:26))
    mydd = as_tibble(t(somedata %>% select(all_of(myvars)) %>% group_by(.data[[variable]]) %>% summarize_all(median)))
    colnames(mydd) = mydd[1,]
    mydd %>% head
    return(mydd)
}

options(repr.plot.width = 12, repr.plot.height = 12, repr.plot.res = 400)

colnames(myd)

1. 'loc'
2. 'era'
3. 'sample'
4. 'desc'
5. 'V1'
6. 'V2'
7. 'V3'
8. 'V4'
9. 'V5'
10. 'V6'
11. 'V7'
12. 'V8'
13. 'V9'
14. 'V10'
15. 'V11'
16. 'V12'
17. 'V13'
18. 'V14'
19. 'V15'
20. 'V16'
21. 'V17'
22. 'V18'
23. 'V19'
24. 'V20'
25. 'V21'
26. 'V22'
27. 'V23'
28. 'V24'
29. 'V25'
30. 'V26'
31. 'Mean'
32. 'Simplified_Culture'
33. 'Location'
34. 'Country'
35. 'Y-Symbol'
36. 'YTree'
37. 'yc'
38. 'y'

mydd = mydfun(myd,"Simplified_Culture")

myfun(mydd[2:nrow(mydd),],2)

[1]  25 132
# A tibble: 6 × 132
  Afanasievo   Africa_East_Pastoralist Aigyrzhal_BA Alan         Altai_MLBA  
  <chr>        <chr>                   <chr>        <chr>        <chr>       
1 " 0.1274820" "-0.3386240"            " 0.0865060" " 0.1109775" " 0.0734160"
2 " 0.0832730" " 0.0883510"            " 0.0304660" " 0.1081540" "-0.1736555"
3 " 0.0384660" "-0.0199875"            "-0.0524200" "-0.0294155" " 0.0543055"
4 " 0.1043290" "-0.0625005"            " 0.0930240" "-0.0025840" " 0.0293930"
5 "-0.0273900" " 0.0032315"            "-0.0960180" "-0.0269280" "-0.0206195"
6 " 0.0398810" "-0.0340245"            " 0.0382080" " 0.0117135" " 0.0047410"
# ℹ 127 more variables: America_Caribbean_Archaic <chr>, Anatolia_BA <chr>,
#   Anatolia_EBA <chr>, Anatolia_East_EBA <chr>, Anatolia_IA <chr>,
#   Andronovo <chr>, Andronovo_Alakul <chr>, Anglian <chr>, Apulia_IA <chr>,
#   Armenia_MLBA <chr>, Asia_CC_BA <chr>, Asia_EC_BIA <chr>,
#   Australia_Aboriginal <chr>, BMAC <chr>, Balkans_BA <chr>,
#   Balkans_EBA <chr>, Balkans_IA <chr>, Baltic_BA <chr>, Baltic_HG <chr>,
#   Bell_Beaker <chr>, Britain_CA_EBA <chr>, Britain_MBA <chr>, …
Bootstrap (r = 0.48)... Done.
Bootstrap (r = 0.6)... Done.
Bootstrap (r = 0.68)... Done.
Bootstrap (r = 0.8)... Done.
Bootstrap (r = 0.88)... Done.
Bootstrap (r = 1.0)... Done.
Bootstrap (r = 1.08)... Done.
Bootstrap (r = 1.2)... Done.
Bootstrap (r = 1.28)... Done.
Bootstrap (r = 1.4)... Done.
       x                y                xend             yend        
 Min.   :  1.00   Min.   :0.00000   Min.   :  1.00   Min.   :0.00000  
 1st Qu.: 33.88   1st Qu.:0.04081   1st Qu.: 33.88   1st Qu.:0.00000  
 Median : 65.62   Median :0.06042   Median : 65.81   Median :0.04585  
 Mean   : 66.50   Mean   :0.11339   Mean   : 66.50   Mean   :0.08357  
 3rd Qu.: 97.81   3rd Qu.:0.11783   3rd Qu.: 98.50   3rd Qu.:0.09655  
 Max.   :132.00   Max.   :0.77335   Max.   :132.00   Max.   :0.77335  
       x                y        label          
 Min.   :  1.00   Min.   :0   Length:132        
 1st Qu.: 33.75   1st Qu.:0   Class :character  
 Median : 66.50   Median :0   Mode  :character  
 Mean   : 66.50   Mean   :0                     
 3rd Qu.: 99.25   3rd Qu.:0                     
 Max.   :132.00   Max.   :0                     

1. A data.frame: 524 × 4

   x	y	xend	yend
   <dbl>	<dbl>	<dbl>	<dbl>
   66.641068	0.77335182	14.743073	0.77335182
   14.743073	0.77335182	14.743073	0.64193417
   14.743073	0.64193417	1.500000	0.64193417
   1.500000	0.64193417	1.500000	0.32706793
   1.500000	0.32706793	1.000000	0.32706793
   1.000000	0.32706793	1.000000	0.00000000
   1.500000	0.32706793	2.000000	0.32706793
   2.000000	0.32706793	2.000000	0.00000000
   14.743073	0.64193417	27.986145	0.64193417
   27.986145	0.64193417	27.986145	0.53837933
   27.986145	0.53837933	3.750000	0.53837933
   3.750000	0.53837933	3.750000	0.17396193
   3.750000	0.17396193	3.000000	0.17396193
   3.000000	0.17396193	3.000000	0.00000000
   3.750000	0.17396193	4.500000	0.17396193
   4.500000	0.17396193	4.500000	0.08838110
   4.500000	0.08838110	4.000000	0.08838110
   4.000000	0.08838110	4.000000	0.00000000
   4.500000	0.08838110	5.000000	0.08838110
   5.000000	0.08838110	5.000000	0.00000000
   27.986145	0.53837933	52.222290	0.53837933
   52.222290	0.53837933	52.222290	0.48608446
   52.222290	0.48608446	24.714355	0.48608446
   24.714355	0.48608446	24.714355	0.28618881
   24.714355	0.28618881	8.734375	0.28618881
   8.734375	0.28618881	8.734375	0.14245899
   8.734375	0.14245899	6.750000	0.14245899
   6.750000	0.14245899	6.750000	0.09914986
   6.750000	0.09914986	6.000000	0.09914986
   6.000000	0.09914986	6.000000	0.00000000
   ⋮	⋮	⋮	⋮
   125.250	0.10980036	124.0	0.10980036
   124.000	0.10980036	124.0	0.00000000
   125.250	0.10980036	126.5	0.10980036
   126.500	0.10980036	126.5	0.07097286
   126.500	0.07097286	125.5	0.07097286
   125.500	0.07097286	125.5	0.03569379
   125.500	0.03569379	125.0	0.03569379
   125.000	0.03569379	125.0	0.00000000
   125.500	0.03569379	126.0	0.03569379
   126.000	0.03569379	126.0	0.00000000
   126.500	0.07097286	127.5	0.07097286
   127.500	0.07097286	127.5	0.05783189
   127.500	0.05783189	127.0	0.05783189
   127.000	0.05783189	127.0	0.00000000
   127.500	0.05783189	128.0	0.05783189
   128.000	0.05783189	128.0	0.00000000
   127.875	0.18335098	130.5	0.18335098
   130.500	0.18335098	130.5	0.11783189
   130.500	0.11783189	129.5	0.11783189
   129.500	0.11783189	129.5	0.04168541
   129.500	0.04168541	129.0	0.04168541
   129.000	0.04168541	129.0	0.00000000
   129.500	0.04168541	130.0	0.04168541
   130.000	0.04168541	130.0	0.00000000
   130.500	0.11783189	131.5	0.11783189
   131.500	0.11783189	131.5	0.05638794
   131.500	0.05638794	131.0	0.05638794
   131.000	0.05638794	131.0	0.00000000
   131.500	0.05638794	132.0	0.05638794
   132.000	0.05638794	132.0	0.00000000

myfun(mydd[2:nrow(mydd),],2,FALSE)

[1]  25 132
# A tibble: 6 × 132
  Afanasievo   Africa_East_Pastoralist Aigyrzhal_BA Alan         Altai_MLBA  
  <chr>        <chr>                   <chr>        <chr>        <chr>       
1 " 0.1274820" "-0.3386240"            " 0.0865060" " 0.1109775" " 0.0734160"
2 " 0.0832730" " 0.0883510"            " 0.0304660" " 0.1081540" "-0.1736555"
3 " 0.0384660" "-0.0199875"            "-0.0524200" "-0.0294155" " 0.0543055"
4 " 0.1043290" "-0.0625005"            " 0.0930240" "-0.0025840" " 0.0293930"
5 "-0.0273900" " 0.0032315"            "-0.0960180" "-0.0269280" "-0.0206195"
6 " 0.0398810" "-0.0340245"            " 0.0382080" " 0.0117135" " 0.0047410"
# ℹ 127 more variables: America_Caribbean_Archaic <chr>, Anatolia_BA <chr>,
#   Anatolia_EBA <chr>, Anatolia_East_EBA <chr>, Anatolia_IA <chr>,
#   Andronovo <chr>, Andronovo_Alakul <chr>, Anglian <chr>, Apulia_IA <chr>,
#   Armenia_MLBA <chr>, Asia_CC_BA <chr>, Asia_EC_BIA <chr>,
#   Australia_Aboriginal <chr>, BMAC <chr>, Balkans_BA <chr>,
#   Balkans_EBA <chr>, Balkans_IA <chr>, Baltic_BA <chr>, Baltic_HG <chr>,
#   Bell_Beaker <chr>, Britain_CA_EBA <chr>, Britain_MBA <chr>, …
Bootstrap (r = 0.48)... Done.
Bootstrap (r = 0.6)... Done.
Bootstrap (r = 0.68)... Done.
Bootstrap (r = 0.8)... Done.
Bootstrap (r = 0.88)... Done.
Bootstrap (r = 1.0)... Done.
Bootstrap (r = 1.08)... Done.
Bootstrap (r = 1.2)... Done.
Bootstrap (r = 1.28)... Done.
Bootstrap (r = 1.4)... Done.
       x                y                xend             yend        
 Min.   :  1.00   Min.   :0.00000   Min.   :  1.00   Min.   :0.00000  
 1st Qu.: 33.88   1st Qu.:0.04081   1st Qu.: 33.88   1st Qu.:0.00000  
 Median : 65.62   Median :0.06042   Median : 65.81   Median :0.04585  
 Mean   : 66.50   Mean   :0.11339   Mean   : 66.50   Mean   :0.08357  
 3rd Qu.: 97.81   3rd Qu.:0.11783   3rd Qu.: 98.50   3rd Qu.:0.09655  
 Max.   :132.00   Max.   :0.77335   Max.   :132.00   Max.   :0.77335  
       x                y        label          
 Min.   :  1.00   Min.   :0   Length:132        
 1st Qu.: 33.75   1st Qu.:0   Class :character  
 Median : 66.50   Median :0   Mode  :character  
 Mean   : 66.50   Mean   :0                     
 3rd Qu.: 99.25   3rd Qu.:0                     
 Max.   :132.00   Max.   :0                     

1. A data.frame: 524 × 4

   x	y	xend	yend
   <dbl>	<dbl>	<dbl>	<dbl>
   66.641068	0.77335182	14.743073	0.77335182
   14.743073	0.77335182	14.743073	0.64193417
   14.743073	0.64193417	1.500000	0.64193417
   1.500000	0.64193417	1.500000	0.32706793
   1.500000	0.32706793	1.000000	0.32706793
   1.000000	0.32706793	1.000000	0.00000000
   1.500000	0.32706793	2.000000	0.32706793
   2.000000	0.32706793	2.000000	0.00000000
   14.743073	0.64193417	27.986145	0.64193417
   27.986145	0.64193417	27.986145	0.53837933
   27.986145	0.53837933	3.750000	0.53837933
   3.750000	0.53837933	3.750000	0.17396193
   3.750000	0.17396193	3.000000	0.17396193
   3.000000	0.17396193	3.000000	0.00000000
   3.750000	0.17396193	4.500000	0.17396193
   4.500000	0.17396193	4.500000	0.08838110
   4.500000	0.08838110	4.000000	0.08838110
   4.000000	0.08838110	4.000000	0.00000000
   4.500000	0.08838110	5.000000	0.08838110
   5.000000	0.08838110	5.000000	0.00000000
   27.986145	0.53837933	52.222290	0.53837933
   52.222290	0.53837933	52.222290	0.48608446
   52.222290	0.48608446	24.714355	0.48608446
   24.714355	0.48608446	24.714355	0.28618881
   24.714355	0.28618881	8.734375	0.28618881
   8.734375	0.28618881	8.734375	0.14245899
   8.734375	0.14245899	6.750000	0.14245899
   6.750000	0.14245899	6.750000	0.09914986
   6.750000	0.09914986	6.000000	0.09914986
   6.000000	0.09914986	6.000000	0.00000000
   ⋮	⋮	⋮	⋮
   125.250	0.10980036	124.0	0.10980036
   124.000	0.10980036	124.0	0.00000000
   125.250	0.10980036	126.5	0.10980036
   126.500	0.10980036	126.5	0.07097286
   126.500	0.07097286	125.5	0.07097286
   125.500	0.07097286	125.5	0.03569379
   125.500	0.03569379	125.0	0.03569379
   125.000	0.03569379	125.0	0.00000000
   125.500	0.03569379	126.0	0.03569379
   126.000	0.03569379	126.0	0.00000000
   126.500	0.07097286	127.5	0.07097286
   127.500	0.07097286	127.5	0.05783189
   127.500	0.05783189	127.0	0.05783189
   127.000	0.05783189	127.0	0.00000000
   127.500	0.05783189	128.0	0.05783189
   128.000	0.05783189	128.0	0.00000000
   127.875	0.18335098	130.5	0.18335098
   130.500	0.18335098	130.5	0.11783189
   130.500	0.11783189	129.5	0.11783189
   129.500	0.11783189	129.5	0.04168541
   129.500	0.04168541	129.0	0.04168541
   129.000	0.04168541	129.0	0.00000000
   129.500	0.04168541	130.0	0.04168541
   130.000	0.04168541	130.0	0.00000000
   130.500	0.11783189	131.5	0.11783189
   131.500	0.11783189	131.5	0.05638794
   131.500	0.05638794	131.0	0.05638794
   131.000	0.05638794	131.0	0.00000000
   131.500	0.05638794	132.0	0.05638794
   132.000	0.05638794	132.0	0.00000000

colnames(myd)

1. 'loc'
2. 'era'
3. 'sample'
4. 'desc'
5. 'V1'
6. 'V2'
7. 'V3'
8. 'V4'
9. 'V5'
10. 'V6'
11. 'V7'
12. 'V8'
13. 'V9'
14. 'V10'
15. 'V11'
16. 'V12'
17. 'V13'
18. 'V14'
19. 'V15'
20. 'V16'
21. 'V17'
22. 'V18'
23. 'V19'
24. 'V20'
25. 'V21'
26. 'V22'
27. 'V23'
28. 'V24'
29. 'V25'
30. 'V26'
31. 'Mean'
32. 'Simplified_Culture'
33. 'Location'
34. 'Country'
35. 'Y-Symbol'
36. 'YTree'
37. 'yc'
38. 'y'

write_csv(myd %>% select(all_of(c("sample","desc","Simplified_Culture","Country","Location","y",paste0("V",2:26)))) %>% arrange(sample),"~/Documents/Ancestry/Raw Data/Archeological Samples/aDNA++.csv")

myd %>% count(Simplified_Culture,sort = TRUE) %>% head()

A data.frame: 6 × 2

	Simplified_Culture	n
	<chr>	<int>
1	Viking	79
2	Bell_Beaker	22
3	Corded_Ware	14
4	Mongol	13
5	Italy_MA	12
6	Xiongnu	12

mavg = as_tibble(import("/Users/timoha/Documents/Ancestry/Genetics/G25/Data/TXT/Global25_PCA_modern_pop_averages_scaled.txt"))

mavg %>% sample_n(10)

A tibble: 10 × 26

V1	V2	V3	V4	V5	V6	V7	V8	V9	V10	⋯	V17	V18	V19	V20	V21	V22	V23	V24	V25	V26
<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	⋯	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
Chamalin	0.1123745	0.1082924	-0.0248901	0.0117748	-0.0428051	0.0148065	0.0116652	-0.0032725	-0.0616546	⋯	-0.0023264	0.0040656	-0.0040194	-0.0058964	0.0038314	0.0013500	0.0027092	0.0080223	0.0097493	-0.0008711
Mayan	0.0567490	-0.2949387	0.1107659	0.0902554	-0.1048547	-0.0156974	-0.2758356	-0.3286347	-0.0140829	⋯	0.0037504	0.0079163	-0.0009773	-0.0013466	-0.0019474	-0.0026024	0.0056176	0.0010037	0.0000344	0.0003594
Greek_Izmir	0.1146199	0.1443068	-0.0024135	-0.0345287	0.0149874	-0.0126059	0.0025145	-0.0015000	-0.0022907	⋯	0.0001989	0.0107827	-0.0001141	0.0059203	-0.0030515	-0.0060393	0.0019289	0.0002095	0.0023496	-0.0030056
German_Hamburg	0.1282853	0.1339901	0.0634672	0.0485260	0.0400617	0.0203755	0.0075618	0.0091083	0.0029597	⋯	0.0032679	-0.0045558	0.0025562	0.0039926	0.0006842	0.0017395	0.0015566	0.0027185	0.0073504	-0.0010354
Saliya_Kerala	0.0443910	-0.0751490	-0.1644250	0.1052990	-0.0738600	0.0571730	-0.0007050	0.0177680	0.0370190	⋯	-0.0022540	-0.0026080	0.0017740	-0.0018850	-0.0003750	0.0082350	0.0000000	0.0038210	-0.0065070	0.0063470
Polish	0.1318405	0.1292694	0.0698685	0.0577382	0.0406754	0.0217127	0.0086781	0.0108626	-0.0009329	⋯	-0.0005595	0.0015455	-0.0000741	0.0026948	0.0012872	-0.0031286	-0.0031185	0.0056063	-0.0033328	-0.0000876
Brahmin_Uttar_Pradesh_East	0.0671557	-0.0287249	-0.1255811	0.1099131	-0.0729366	0.0631489	0.0006379	0.0081756	0.0258869	⋯	0.0006250	-0.0049920	0.0020630	-0.0039146	-0.0104693	-0.0019966	-0.0001060	0.0031164	0.0043723	-0.0076639
Aymara	0.0569120	-0.3127830	0.1191700	0.0930240	-0.1147910	-0.0145020	-0.3015190	-0.3592930	-0.0096130	⋯	0.0034470	0.0069100	0.0002530	-0.0017600	0.0025010	-0.0014970	0.0040810	0.0036970	0.0003610	-0.0075440
Palestinian_Beit_Sahour	0.0876435	0.1467440	-0.0541165	-0.0849490	-0.0156955	-0.0231480	-0.0054050	-0.0050765	0.0117600	⋯	0.0144525	0.0098440	0.0019635	-0.0001255	-0.0011255	-0.0016845	0.0038950	0.0035125	0.0053620	0.0033525
Kazakh_Xinjiang	0.0627164	-0.2412896	0.0479318	-0.0097869	-0.0389302	-0.0148092	0.0107871	0.0141225	-0.0070152	⋯	0.0044285	0.0019687	0.0002659	0.0048142	0.0066404	-0.0112302	-0.0048099	-0.0097118	-0.0006988	-0.0012094

mavg = mydfun(mavg,"V1")

mavg %>% head(10)

A tibble: 10 × 856

Abazin	Abkhasian	Abkhasian_Gudauta	Adygei	Aeta	Afrikaner	Agta	Ahiska	Akha	Akhvakh	⋯	Yemenite_Mahra	Yi	Yoruba	Yugur	Yukagir_Forest	Yukagir_Tundra	Yukpa	Yuku	Zapotec	Zhuang
<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	⋯	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>	<chr>
Abazin	Abkhasian	Abkhasian_Gudauta	Adygei	Aeta	Afrikaner	Agta	Ahiska	Akha	Akhvakh	⋯	Yemenite_Mahra	Yi	Yoruba	Yugur	Yukagir_Forest	Yukagir_Tundra	Yukpa	Yuku	Zapotec	Zhuang
0.1079895	0.1092703	0.1109775	0.1128114	-0.0165040	0.1158925	-0.0210570	0.1115467	0.0163146	0.1166687	⋯	0.0542556	0.0201087	-0.6300625	0.0275310	0.0858226	0.0404072	0.0546350	0.0346022	0.0539522	0.0174529
0.0755300	0.1166731	0.1218635	0.1033583	-0.3330935	0.1294338	-0.3315700	0.1293107	-0.4336311	0.1068846	⋯	0.1360809	-0.4393857	0.0625011	-0.4163672	-0.1387214	-0.4049061	-0.3158300	-0.3748996	-0.2983626	-0.4492026
-0.0231456	-0.0478942	-0.0494025	-0.0297507	-0.0923945	0.0524198	-0.0956000	-0.0519170	-0.0206411	-0.0218100	⋯	-0.0682589	-0.0072910	0.0221130	0.0112194	0.1010680	0.1285711	0.1112510	0.0202640	0.1125328	-0.0364969
-0.0175631	-0.0406621	-0.0369835	-0.0192364	0.0332690	0.0390096	0.0342380	-0.0485577	-0.0504526	0.0188147	⋯	-0.1211978	-0.0485577	0.0167079	-0.0554349	0.0299098	-0.0016381	0.0994840	-0.0395244	0.0919258	-0.0660356
-0.0323136	-0.0389473	-0.0386225	-0.0334420	0.1041730	0.0370557	0.1044810	-0.0333393	0.0812767	-0.0406229	⋯	-0.0037272	0.0544713	0.0005035	0.0371222	-0.0431462	-0.1307937	-0.1110980	0.0104019	-0.1038348	0.1187230
0.0003835	-0.0065696	-0.0058565	-0.0020761	-0.0065540	0.0149967	-0.0139445	-0.0099470	0.0360884	0.0127360	⋯	-0.0563050	0.0194290	0.0124741	0.0147812	-0.0209168	-0.0729101	-0.0206380	0.0043972	-0.0159524	0.0559021
0.0081666	0.0113851	0.0110455	0.0091653	0.0023500	0.0038134	0.0001175	0.0050133	0.0008930	0.0052484	⋯	-0.0130301	0.0025850	-0.0444170	0.0064921	0.0127848	0.0043477	-0.3045740	0.0085464	-0.2672540	-0.0010575
-0.0031151	-0.0063329	-0.0042690	-0.0032051	-0.0139605	0.0064823	-0.0091150	-0.0066150	-0.0035383	-0.0025768	⋯	-0.0088204	-0.0001540	0.0477673	0.0017163	0.0231682	0.0111589	-0.3636770	0.0053614	-0.3242634	-0.0083203
-0.0499037	-0.0615163	-0.0608460	-0.0516992	0.0047040	0.0066750	0.0070565	-0.0503127	-0.0065107	-0.0612037	⋯	0.0628798	-0.0064767	-0.0488813	-0.0076058	0.0153802	0.0224101	-0.0108400	-0.0059789	-0.0066264	-0.0177823

myfun(mavg[2:nrow(mavg),],1)

[1]  25 856
# A tibble: 6 × 856
  Abazin   Abkhasian Abkhasian_Gudauta Adygei Aeta  Afrikaner Agta  Ahiska Akha 
  <chr>    <chr>     <chr>             <chr>  <chr> <chr>     <chr> <chr>  <chr>
1 " 0.107… " 0.1092… " 0.1109775"      " 0.1… "-0.… " 0.1158… "-0.… " 0.1… " 0.…
2 " 0.075… " 0.1166… " 0.1218635"      " 0.1… "-0.… " 0.1294… "-0.… " 0.1… "-0.…
3 "-0.023… "-0.0478… "-0.0494025"      "-0.0… "-0.… " 0.0524… "-0.… "-0.0… "-0.…
4 "-0.017… "-0.0406… "-0.0369835"      "-0.0… " 0.… " 0.0390… " 0.… "-0.0… "-0.…
5 "-0.032… "-0.0389… "-0.0386225"      "-0.0… " 0.… " 0.0370… " 0.… "-0.0… " 0.…
6 " 0.000… "-0.0065… "-0.0058565"      "-0.0… "-0.… " 0.0149… "-0.… "-0.0… " 0.…
# ℹ 847 more variables: Akhvakh <chr>, Alawite <chr>, Albanian <chr>,
#   Alevi_Dersim <chr>, Algerian <chr>, Altaian <chr>, Amerindian_North <chr>,
#   Ami <chr>, Andian_A <chr>, Andian_B <chr>, Arain <chr>,
#   Armenian_Aintab <chr>, Armenian_Ararat <chr>, Armenian_Artsakh <chr>,
#   Armenian_Erzurum <chr>, Armenian_Gesaria <chr>, Armenian_Gurin <chr>,
#   Armenian_Hemsheni <chr>, Armenian_Parspatunik <chr>, Armenian_Syunik <chr>,
#   Armenian_Urfa <chr>, Arora <chr>, Ashkenazi_Belarussia <chr>, …
Bootstrap (r = 0.48)... Done.
Bootstrap (r = 0.6)... Done.
Bootstrap (r = 0.68)... Done.
Bootstrap (r = 0.8)... Done.
Bootstrap (r = 0.88)... Done.
Bootstrap (r = 1.0)... Done.
Bootstrap (r = 1.08)... Done.
Bootstrap (r = 1.2)... Done.
Bootstrap (r = 1.28)... Done.
Bootstrap (r = 1.4)... Done.
       x               y                 xend            yend        
 Min.   :  1.0   Min.   :0.004265   Min.   :  1.0   Min.   :0.00000  
 1st Qu.:216.5   1st Qu.:0.014953   1st Qu.:216.0   1st Qu.:0.00000  
 Median :429.8   Median :0.023124   Median :429.4   Median :0.01707  
 Mean   :428.8   Mean   :0.049189   Mean   :428.8   Mean   :0.03657  
 3rd Qu.:641.9   3rd Qu.:0.045738   3rd Qu.:642.0   3rd Qu.:0.03414  
 Max.   :856.0   Max.   :1.108674   Max.   :856.0   Max.   :1.10867  
       x               y        label          
 Min.   :  1.0   Min.   :0   Length:856        
 1st Qu.:214.8   1st Qu.:0   Class :character  
 Median :428.5   Median :0   Mode  :character  
 Mean   :428.5   Mean   :0                     
 3rd Qu.:642.2   3rd Qu.:0                     
 Max.   :856.0   Max.   :0                     

1. A data.frame: 3420 × 4

   x	y	xend	yend
   <dbl>	<dbl>	<dbl>	<dbl>
   165.90388	1.10867401	47.85533	1.10867401
   47.85533	1.10867401	47.85533	0.74460198
   47.85533	0.74460198	15.96066	0.74460198
   15.96066	0.74460198	15.96066	0.56918529
   15.96066	0.56918529	4.59375	0.56918529
   4.59375	0.56918529	4.59375	0.23305611
   4.59375	0.23305611	1.50000	0.23305611
   1.50000	0.23305611	1.50000	0.12127535
   1.50000	0.12127535	1.00000	0.12127535
   1.00000	0.12127535	1.00000	0.00000000
   1.50000	0.12127535	2.00000	0.12127535
   2.00000	0.12127535	2.00000	0.00000000
   4.59375	0.23305611	7.68750	0.23305611
   7.68750	0.23305611	7.68750	0.16298713
   7.68750	0.16298713	4.68750	0.16298713
   4.68750	0.16298713	4.68750	0.03274527
   4.68750	0.03274527	3.50000	0.03274527
   3.50000	0.03274527	3.50000	0.01477208
   3.50000	0.01477208	3.00000	0.01477208
   3.00000	0.01477208	3.00000	0.00000000
   3.50000	0.01477208	4.00000	0.01477208
   4.00000	0.01477208	4.00000	0.00000000
   4.68750	0.03274527	5.87500	0.03274527
   5.87500	0.03274527	5.87500	0.01780827
   5.87500	0.01780827	5.00000	0.01780827
   5.00000	0.01780827	5.00000	0.00000000
   5.87500	0.01780827	6.75000	0.01780827
   6.75000	0.01780827	6.75000	0.01595316
   6.75000	0.01595316	6.00000	0.01595316
   6.00000	0.01595316	6.00000	0.00000000
   ⋮	⋮	⋮	⋮
   848.7500	0.05499006	848.0000	0.05499006
   848.0000	0.05499006	848.0000	0.00000000
   848.7500	0.05499006	849.5000	0.05499006
   849.5000	0.05499006	849.5000	0.02508867
   849.5000	0.02508867	849.0000	0.02508867
   849.0000	0.02508867	849.0000	0.00000000
   849.5000	0.02508867	850.0000	0.02508867
   850.0000	0.02508867	850.0000	0.00000000
   848.0312	0.20355211	852.6875	0.20355211
   852.6875	0.20355211	852.6875	0.14410124
   852.6875	0.14410124	851.5000	0.14410124
   851.5000	0.14410124	851.5000	0.03340822
   851.5000	0.03340822	851.0000	0.03340822
   851.0000	0.03340822	851.0000	0.00000000
   851.5000	0.03340822	852.0000	0.03340822
   852.0000	0.03340822	852.0000	0.00000000
   852.6875	0.14410124	853.8750	0.14410124
   853.8750	0.14410124	853.8750	0.07943262
   853.8750	0.07943262	853.0000	0.07943262
   853.0000	0.07943262	853.0000	0.00000000
   853.8750	0.07943262	854.7500	0.07943262
   854.7500	0.07943262	854.7500	0.05779667
   854.7500	0.05779667	854.0000	0.05779667
   854.0000	0.05779667	854.0000	0.00000000
   854.7500	0.05779667	855.5000	0.05779667
   855.5000	0.05779667	855.5000	0.01905582
   855.5000	0.01905582	855.0000	0.01905582
   855.0000	0.01905582	855.0000	0.00000000
   855.5000	0.01905582	856.0000	0.01905582
   856.0000	0.01905582	856.0000	0.00000000